Learning method, learning apparatus, and recording medium having stored therein learning program

Abstract

A machine learning model, in which core tensors are generated, is trained by a computer. The computer performs a process including: extracting, from a plurality of items of pseudo training data generated from a plurality of items of training data for the machine learning model, a plurality of items of determined pseudo training data that are determined as pseudo training data that promotes training of the machine learning model; and training the machine learning model by using the plurality of items of determined pseudo training data.

What is claimed is: 
     
         1 . A non-transitory computer-readable recording medium having stored therein a learning program for causing a computer to execute a process, the process comprising:
 extracting, from a plurality of items of pseudo training data generated from a plurality of items of training data for a machine learning model in which core tensors are generated, a plurality of items of determined pseudo training data that are determined as pseudo training data that promotes training of the machine learning model; and   training the machine learning model by using the plurality of items of determined pseudo training data.   
     
     
         2 . The non-transitory computer-readable recording medium according to  claim 1 , wherein the plurality of items of pseudo training data are generated by using, as learning target data, incorrectly identified training data in cross-testing performed on the plurality of items of training data. 
     
     
         3 . The non-transitory computer-readable recording medium according to  claim 2 , wherein the extracting includes designating, as a set of candidate data of determined pseudo training data, a set of pseudo training data about which it is determined that the core tensors are changed and extracting the plurality of items of determined pseudo training data from the set of candidate data by using a determiner in which training data of a particular type similar to a type of incorrectly identified training data is designated as a positive example while training data of another particular type different from the type of incorrectly identified training data and the incorrectly identified training data are designated as negative examples. 
     
     
         4 . The non-transitory computer-readable recording medium according to  claim 3 , wherein the extracting includes evaluating accuracy of cross-testing by using training data together with the set of candidate data that is added, and when it is determined that the accuracy is improved, extracting the set of candidate data as determined pseudo training data. 
     
     
         5 . A learning method for causing a computer to execute a process, the process comprising:
 extracting, from a plurality of items of pseudo training data generated from a plurality of items of training data for a machine learning model in which core tensors are generated, a plurality of items of determined pseudo training data that are determined as pseudo training data that promotes training of the machine learning model; and   training the machine learning model by using the plurality of items of determined pseudo training data.   
     
     
         6 . A learning apparatus to execute a process for training a machine learning model, the learning apparatus comprising:
 a memory, and   a processor coupled to the memory and performing a process including:   extracting, from a plurality of items of pseudo training data generated from a plurality of items of training data for the machine learning model in which core tensors are generated, a plurality of items of determined pseudo training data that are determined as pseudo training data that promotes training of the machine learning model; and   training the machine learning model by using the plurality of items of determined pseudo training data.